Computer systems have forever changed modern society and industry. In recent times, technological innovations relating to computers have increasingly become intertwined with daily activities. For example, it has become commonplace for humans and machines—such as computers, to interact and/or communicate in order to affect real-world outcomes. Many of these interactions involve transactions wherein computers are directed by humans via some form of communications input such as from keyboards, speech, and/or vision sensors. As an example, it is common to interact over the telephone with a voice-activated system to conduct transactions associated with airline travel, banking, and shopping to name but a few applications. Other examples involve more sophisticated applications such as in control environments wherein operators direct computer-controlled systems via spoken and/or other human commands. Unfortunately, public enthusiasm for interacting with automated systems, such as provided by automated speech recognition systems has been tempered by the common experience of frustrating and costly recognition of errors associated with these systems.
A common frustration with human-machine interactions relates to machines making improper choices based upon uncertain/ambiguous communications directed to the machine. These uncertainties may involve differences in audibility (e.g., background noise, input decibel level), speech patterns, dialects, and word choices, for example. Many conventional systems fail to adequately account for uncertainty, however. These systems will often erroneously conduct transactions and/or affect real world outcomes with little or no consideration regarding the costs associated with making a mistake. These systems also generally fail to assess internal states of uncertainty before making their decisions. Another problem associated with conventional systems is that utterances (e.g., discrete commands directed over time at computer) are generally treated as independent events wherein previous utterances are generally unaccounted for when determining a user's current command/instruction. This also may lead to increased misunderstanding and thus frustration between humans and computers.
In view of the above problems associated with conventional speech and/or other communications recognition systems, there is an unsolved need for a system and/or methodology to facilitate improved decision-making by computers based upon ambiguous and/or uncertain human command utterances and environments.